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URL: https://time.com/collections/best-inventions-2024/7094933/google-deepmind-alphafold-3/

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Finding Cures

Booth is a reporter at TIME.

7PNM - Spike protein of a common cold virus (Coronavirus OC43): AlphaFold 3’s structural prediction for a spike protein (blue) of a cold virus as it interacts with antibodies (turquoise) and simple sugars (yellow), accurately matches the true structure (gray). The animation shows the protein interacting with an antibody, then a sugar. Advancing our knowledge of such immune-system processes helps better understand coronaviruses, including COVID-19, raising possibilities for improved treatments.

7PNM - Spike protein of a common cold virus (Coronavirus OC43): AlphaFold 3’s structural prediction for a spike protein (blue) of a cold virus as it interacts with antibodies (turquoise) and simple sugars (yellow), accurately matches the true structure (gray). The animation shows the protein interacting with an antibody, then a sugar. Advancing our knowledge of such immune-system processes helps better understand coronaviruses, including COVID-19, raising possibilities for improved treatments.Google Deepmind

In 2020, Google DeepMind unveiled AlphaFold 2, a machine-learning algorithm that in 2022 would prove capable of predicting, with near-experimental accuracy, the structure of nearly every known protein. The breakthrough recently earned co-founder and CEO Demis Hassabis and AlphaFold team lead John Jumper a Nobel prize in chemistry. Now there’s a sequel: AlphaFold 3, released in May, predicts how proteins interact with DNA and other molecules. “AlphaFold 2 was a story about proteins,” Jumper says. “AlphaFold 3 is about how proteins talk to everything else.” Many in pharmaceuticals and medicine believe that the new model’s predictions will accelerate research and drive faster drug discovery.

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